Mortality Risk-Stratified Septic Serum Depresses Contractility and Mitochondrial Function in Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes [SOMAscan]
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE314568 | GEO | 2026/09/01
REPOSITORIES: GEO
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Project description:We sought to investigate human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) exposed to mortality risk-stratified septic serum as a model of cardiomyocyte pathobiology in septic shock. We stratified previously banked serum from children with septic shock by Pediatric Sepsis Biomarker Risk Model (PERSEVERE) II mortality probability as low, intermediate, or high risk. We treated iPSC-CM spheroids with 10% v/v diluted risk-stratified septic serum and defined contractile and mitochondrial functional and transcriptomic responses. High-risk septic serum reversibly depressed iPSC-CM contractility as measured by percent shortening and spontaneous beat frequency, while low-risk septic serum had no impact. Further, high-risk septic serum depressed measures of mitochondrial function and drove distinct patterns of gene expression. These findings validate mortality risk-stratified septic serum exposure of iPSC-CMs as a human-relevant translational platform to interrogate mechanisms of myocardial dysfunction in septic shock.
2026-09-01 | GSE314561 | GEO
Project description:Goal of the experiment: To identify correlated genes, pathways and groups of patients with systemic inflammatory response syndrome and septic shock that is indicative of biologically important processes active in these patients. Background: We measured gene expression levels and profiles of children with systemic inflammatory response syndrome (SIRS) and septic shock as a means for discovering patient sub-groups and gene signatures that are active in disease-affected individuals and potentially in patients with poor outcomes. Methods: Microarray and bioinformatics analyses of 123 microarray chips representing whole blood derived RNA from controls, children with SIRS, and children with septic shock. Results: A discovery-based filtering approach was undertaken to identify genes whose expression levels were altered in patients with SIRS or septic shock. Clustering of these genes identified 3 Major and several minor sub-groups of patients with SIRS or septic shock. The three groups differed with respect to incidence of septic shock and trended toward differences in mortality. Statistical analyses demonstrated that 6,435 gene probes were differentially regulated between the three patient sub-groups (false discovery rate < 0.001%). Of these gene probes, 623 gene probes within 7 major gene ontologies accounted for the majority of group differentiation. Network analyses of these 623 gene probes demonstrated 5 major gene networks that were differentially expressed between the 3 groups. Statistical comparison of septic shock survivors and non-survivors identified one major gene network that was under expressed in a high fraction of the non-survivors and identified potential biomarkers for poor outcome. Conclusions: This is the first genome-level demonstration of pediatric patient sub-groups with SIRS and septic shock. The sub-groups differ clinically and differentially express 5 major gene networks. We have identified gene signatures and potential biomarkers associated with poor outcome in children with septic shock. These data represent a major advancement in our genome-level understanding of pediatric SIRS and septic shock. Experiment Overall Design: Children < 10 years of age admitted to the pediatric intensive care unit and meeting the criteria for either SIRS or septic shock were eligible for the study. SIRS and septic shock were defined based on pediatric-specific criteria. We did not use separate categories of "sepsis" or "severe sepsis". Patients meeting criteria for "sepsis" or "severe sepsis" were placed in the categories of SIRS and septic shock, respectively, for study purposes. Control patients were recruited from the outpatient or inpatient departments of the participating institutions using the following exclusion criteria: a recent febrile illness (within 2 weeks), recent use of anti-inflammatory medications (within 2 weeks), or any history of chronic or acute disease associated with inflammation. Experiment Overall Design: After obtaining informed consent, blood samples were obtained on Day 1 of the study, and when possible on Day 3 of the study. Blood samples were divided for RNA extraction and isolation of serum. Severity of illness was calculated based on the PRISM III score. Organ failure was defined based on pediatric-specific criteria. Annotated clinical and laboratory data were collected daily while in the intensive care unit. Study patients were placed in the study categories of SIRS or Septic Shock on Day 1 of the study. On Day 3 of the study, patients were classified as SIRS, Septic Shock, or SIRS resolved (no longer meeting criteria for SIRS). All study patients were followed for 28 days to determine mortality or survival. Clinical, laboratory, and biological data were entered and stored using a web-based data base developed locally.
2008-06-13 | E-GEOD-4607 | biostudies-arrayexpress
Project description:Background Hypertrophic cardiomyopathy (HCM) is defined clinically by pathological left ventricular hypertrophy (LVH). We have previously developed a plasma proteomics biomarker panel that correlates with clinical markers of disease severity and sudden cardiac death (SCD) risk in adult patients with HCM. The aim of this study was to validate the adult biomarkers and perform new discovery proteomics in childhood-onset HCM. Methods Fifty-nine protein biomarkers were identified from an exploratory plasma proteomics screen in children with HCM and augmented into our existing multiplexed targeted liquid chromatography-tandem/mass spectrometry-based assay. The association of these biomarkers with clinical phenotypes and outcomes was prospectively tested in plasma collected from 148 children with HCM and 50 healthy controls. Machine learning techniques were used to develop novel paediatric plasma proteomic biomarker panels.Results Four previously validated adult HCM markers (Aldolase Fructose-Bisphosphate A, Complement C3a, Talin-1 and Thrombospondin 1) and three new markers (Glycogen Phosphorylase B, Lipoprotein A and Profilin 1) were elevated in paediatric HCM. Using supervised machine learning applied to training (n=137) and validation cohorts (n=61), this 7-biomarker panel differentiated HCM from healthy controls with an area under the curve of 1.0 in the training dataset (sensitivity 100% [95% confidence interval: 95–100]; specificity 100% [96–100]) and 0.82 in the validation dataset (sensitivity 75% [59–86]; specificity 88% [75–94]). Reduced circulating levels of 4 other peptides (Apolipoprotein L1-, Complement 5b-, Immunoglobulin Heavy Constant Epsilon- and Serum Amyloid A4-peptide) found in children with high SCD risk provided complete separation from the low and intermediate risk groups, and predicted mortality and adverse cardiovascular outcomes (hazard ratio 1.53 [1.2 –2.0], p = 0.001). ConclusionIn children, a 7-biomarker proteomics panel can distinguish HCM from controls with high sensitivity and specificity, and a second 4-biomarker panel identifies those at high risk of adverse outcomes, including sudden cardiac death.
2024-07-03 | PXD045304 | Pride
Project description:Severe infections and sepsis is an increasing clinical problem that cause prolonged morbidity and substantial mortality. At present, antibiotics are essentially the only pharmacological treatment for sepsis. The incidence of antibiotic resistance is increasing and it is therefore critical to find new therapies for sepsis. Staphylococcus aureus (S. aureus) is a major cause of septic mortality. Neutrophils play a major role in defense against bacterial infections. We have recently shown that a saturated high fat diet decreases survival in septic mice, but the mechanisms behind remain elusive. The aim of the present study was to investigate how the dietary fat composition affects survival and neutrophils function after experimental septic infection in mice. We found that, after S. aureus infection, mice fed polyunsaturated high fat diet (HFD/P) for 8 weeks had increased septic survival and decreased bacterial load compared with mice fed saturated HFD (HFD/S), and similar to that of mice given low fat diet (LFD). Furthermore, uninfected mice fed HFD/P had increased number of Ly6G+ neutrophils in bone marrow. In addition, mice fed HFD/P had a higher number Ly6G+ neutrophils recruited to the site of inflammation after peritoneal injection of thioglycollate. In conclusion, polyunsaturated dietary fat increased both survival and the efficiency of the bacterial clearance during septic S. aureus infection. Moreover, this diet enhanced the number and chemotaxis of neutrophils, a key component of the immune response to S. aureus infections. Mice (non-infected) fed saturated high fat diet, low fat diet, or polyunsaturated high fat diet
2014-11-24 | E-GEOD-58358 | biostudies-arrayexpress
Project description:<p>Sepsis is a life-threatening syndrome requiring aggressive management, and novel noninvasive biomarkers to enable risk stratification of sepsis with high confidence and to predict the sepsis-related outcomes are urgently needed. </p><p>A mass spectrometry–based quantitative method was used to analyze the abundance of serum amino acids between patients with sepsis and healthy controls (HC), in order to characterize alterations in amino acid profiles associated with sepsis. In addition, multiple machine learning methods were applied to construct a prognostic prediction model for patients with sepsis. The predictive performance was assessed and the feature contributions were screened, followed by the development of an explainable prognostic prediction panel for sepsis.</p><p>Sixty participants in the HC group and 172 patients in the sepsis group (82 patients with septic shock) were enrolled in this study. A discernible segregation trend in amino acid profiles between the HC and sepsis groups was disclosed, meanwhile the abundance of amino acids differed significantly among the HC, septic shock, and non-septic shock groups, indicating that amino acids could differentiate patients with sepsis from the HC group with good diagnostic performance. Then, 172 patients in the sepsis group were assigned to training and validation sets, and 130 patient patients with sepsis in external test set were collected. Five machine learning models (deephit, piecewise constant hazard, probability mass function, resource selection function, and extreme gradient boosting) were afterwards used and the deephit model was selected according to greater area under the curve and clinical benefits in the training, validation, and test sets. In the process of reducing features based on feature importance ranking, the Deephit model based on five features had the best ability to predict survival probability and an optimized Deephit model with screening five features including glutamine, glycine, lysine, pyroglutamic acid and proline was successfully developed to predict the prognostic risk probability for patients with sepsis.</p>
2026-05-24 | MTBLS14567 | MetaboLights
Project description:Goal of the experiment: To identify correlated genes, pathways and groups of patients with systemic inflammatory response syndrome and septic shock that is indicative of biologically important processes active in these patients. Background: We measured gene expression levels and profiles of children with systemic inflammatory response syndrome (SIRS) and septic shock as a means for discovering patient sub-groups and gene signatures that are active in disease-affected individuals and potentially in patients with poor outcomes. Methods: Microarray and bioinformatics analyses of 123 microarray chips representing whole blood derived RNA from controls, children with SIRS, and children with septic shock. Results: A discovery-based filtering approach was undertaken to identify genes whose expression levels were altered in patients with SIRS or septic shock. Clustering of these genes identified 3 Major and several minor sub-groups of patients with SIRS or septic shock. The three groups differed with respect to incidence of septic shock and trended toward differences in mortality. Statistical analyses demonstrated that 6,435 gene probes were differentially regulated between the three patient sub-groups (false discovery rate < 0.001%). Of these gene probes, 623 gene probes within 7 major gene ontologies accounted for the majority of group differentiation. Network analyses of these 623 gene probes demonstrated 5 major gene networks that were differentially expressed between the 3 groups. Statistical comparison of septic shock survivors and non-survivors identified one major gene network that was under expressed in a high fraction of the non-survivors and identified potential biomarkers for poor outcome. Conclusions: This is the first genome-level demonstration of pediatric patient sub-groups with SIRS and septic shock. The sub-groups differ clinically and differentially express 5 major gene networks. We have identified gene signatures and potential biomarkers associated with poor outcome in children with septic shock. These data represent a major advancement in our genome-level understanding of pediatric SIRS and septic shock. Keywords: Septic shock, SIRS, pediatrics, outcome, infection, inflammation
2006-04-30 | GSE4607 | GEO
Project description:Normal children, children with SIRS, children with sepsis, and children with septic shock. Objectives: To advance our biological understanding of pediatric septic shock, we measured the genome-level expression profiles of critically ill children representing the systemic inflammatory response syndrome (SIRS), sepsis, and septic shock spectrum. Experiment Overall Design: Prospective observational study involving microarray-based bioinformatics.
2009-01-05 | E-GEOD-13904 | biostudies-arrayexpress
Project description:Lung cancer is the leading cause of cancer death worldwide. Low-dose computed tomography screening (LDCT) was recently shown to anticipate the time of diagnosis, thus reducing lung cancer mortality. We identifed a serum microRNA signature (the miR-Test) that could identify the optimal target population for LDCT screening. Here, we performed a large-scale validation study of the miR-Test in high-risk individuals enrolled in the Continuous Observation of Smoking Subjects (COSMOS) lung cancer screening program. RT-qPCR of circulating microRNA purified from serum samples. Trizol-LS and miRNEASY Mini kit (Qiagen) were used for miRNA purification. Custom TaqMan® Low Density Array microRNA Custom Panel (Life Technologies) was used to screen serum circulating microRNA.
2016-02-29 | E-GEOD-76462 | biostudies-arrayexpress
Project description:Normal children, children with SIRS, children with sepsis, and children with septic shock. Objectives: To advance our biological understanding of pediatric septic shock, we measured the genome-level expression profiles of critically ill children representing the systemic inflammatory response syndrome (SIRS), sepsis, and septic shock spectrum. Keywords: Normal vs diseased
2008-12-12 | GSE13904 | GEO
Project description:Purpose: Neuroblastoma is characterized by substantial clinical heterogeneity. Despite intensive treatment, the survival rates of high-risk neuroblastoma patients are still disappointingly low. Somatic chromosomal copy number aberrations have been shown to be associated with patient outcome, particularly in low- and intermediate-risk neuroblastoma patients. To improve outcome prediction in high-risk neuroblastoma, we aimed to design a prognostic classification method based on copy number aberrations. Methods: In an international collaboration, normalized high-resolution DNA copy number data (arrayCGH and SNP arrays) from 556 high-risk neuroblastomas obtained at diagnosis were collected from nine collaborative groups and segmented using the same method. We applied logistic and Cox proportional hazard regression to identify genomic aberrations associated with poor outcome. Results: In this study, we identified two types of copy number aberrations that are associated with extremely poor outcome. (i) Distal 6q losses were detected in 5.9% of patients and were associated with a ten-year survival probability of only 3.4%. (ii) Amplifications of regions not encompassing the MYCN locus were detected in 18% of patients and were associated with a ten-year survival probability of only 5.8%. Conclusion: Using a unique large copy number dataset of high-risk neuroblastoma cases, we identified a small subset of high-risk neuroblastoma patients with extremely low survival probability that might be eligible for inclusion in clinical trials of new therapeutics. The amplicons may also nominate alternative treatments that target the amplified genes.
2018-03-13 | GSE103123 | GEO